In numerous service industries today, fraud detection plays an important and prominent role. By way of example, and not limitation, industries that use fraud detection systems as an important component of their businesses include financial service industries, e-commerce retailers, and telecommunication industries.
The role of fraud detection is critical today because of high monetary losses associated with fraud. For example, for the 2006 calendar year, it is estimated that in the U.S. and Canada, over $3 billion dollars in revenue was lost in e-commerce payment transactions due to fraud. While this figure alone is significant, it represents only a fraction of the total fraud annual losses. Revenue losses from fraud are occurring despite companies using several fraud detection tools that aim to protect companies and customers from various forms of fraud. Examples of some common fraud detection tools include address verification services (AVS) and card verification numbers (CVN).
What adds to the cost of fraud further is the current efficiency of fraud detection. In e-commerce payment transactions, it is estimated that companies reject nearly 4 otherwise valid transactions for each fraudulent transaction rejected. This creates a backlash of customer dissatisfaction for each otherwise valid transaction that is rejected. Also, despite automation advances in fraud detection systems, the majority of companies use some level of manual review, which further adds to the cost of combating fraud.
In operation, current automated fraud detection systems function by identifying possible fraud events. To identify fraud events, automated fraud detection systems collect and analyze transaction information and other indicating data to identify certain patterns of behavior and/or characteristics that have been associated with fraud. These patterns and/or characteristics may be used to create fraud models. When a transaction is evaluated against a fraud model, and the model provides a statistical indication that the transaction may be fraudulent, the transaction can be marked as potentially fraudulent. Once a possible fraud event is identified, the associated transaction or request is either accepted, rejected, sent to manual review, or assigned for further automated review. The determination of how a particular fraud event or a class of fraud events is handled is based upon rules established in an automated screening system.
Despite current fraud detection systems, one limitation of such systems is their emphasis on traditional transaction details when detecting fraud. Traditional indicators may include such things as per card transaction information, recent transaction history, verification errors, device identification, and common customer care requests. To provide further detail of such traditional indicators, examples of per card transaction information may include: transaction value, type of goods purchased, transaction channel of purchase (point-of-sale or internet), physical presence or absence of a customer's card at purchase (card swipe, card chip, or manually entering card number), and transaction country. Examples of verification errors may include: failed personal identification number (PIN) on an automatic teller machine (ATM), and failed PIN on a chip-and-pin point-of-sale (POS) terminal. Examples of device identification indicators may include IP address tracking. For instance, where a customer's account shows access to self-care via an internet device with a given IP address, later access by an alternate IP address, or use of the same IP address for accessing multiple user accounts, may suggest fraud. Examples of common customer care requests may include: address change requests, statement requests, credit limit increase requests, balance inquiries, additional card requests, and account open or close requests.
Another challenge in the market for fraud detection systems is the dynamic nature of fraud. Those who commit fraudulent activities continue to invent new ways to circumvent fraud detection systems. Hence, in some instances, an initially effective fraud model can become ineffective without frequent updates to the underlying aspects of that model.
Based on the statistics for fraud losses and the cost of combating fraud, a substantial need exists to improve the detection of fraud events. Furthermore, it is desirable that such improvements include reducing the number of incorrectly identified fraudulent events and/or reducing the cost to identify fraud events. Certain embodiments described below are designed to provide a solution to certain weaknesses in fraud detection technology as set forth above.